What are the top contributions from neuroscience to AI and viceversa? How much progress has been made from the interaction between these two fields?
Understanding a human brain fully, how hippocampus and neocortex works would surely help AI enthusiasts to make better AI algorithms with superior memory, ability to learn and finding what precisely intelligence, feelings and conciousness are by itself. The AI looks at psysiological and anatomical data as a source of suggestions regarding possible mechanisms and processes that could be used. Still AI doesn't deal that much from neuroscience.

From brain to AI : edge-detection using zero-crossing at multiple scales is a decent example where physiological data are used to suggest a solution to a fundamental problem in computer vision. In computational vision, a problem is the initial organization and representation of the input image. The goal is to transform the measurements of light-intensity level made by photoreceptors eyes' have, into a representation that is more concise. One of the most successful approaches to the problem had its origin in physiological studies of the processing that takes place at the level of the retinal ganglion cells which can be thought of as filtering the image with center-surround receptive fields whose cross-section can be approximated analytically by the second derivative of a gaussian. There had been proven research that a powerful edge detection scheme could be based upon the operation of such cells.

From AI to brain : Integration of motion perception measurement is a worth-mentioning example. It originates in studies of problem of measuring the motion of edges and contours in the image. Such motion can not be determined on the basis of purely local measurements. The underlying reason is 'aperture problem'. Selective visual attention and long-range motion analytics are another two fields where use of computational cosiderations are illustrated in the design and interpretation of physiological studies.